Assessing Musical Similarity for Computational Music Creativity

Abstract

Computationally creative systems require semantic information when reflecting or self reasoning on their output. In this paper we outline the design of a computationally creative musical performance system aimed at producing virtuosic interpretations of musical pieces and provide an overview of its implementation. The case-based reasoning part of the system relies on a measure of musical similarity based on the FANTASTIC and SynPy toolkits that provide melodic and syncopated rhythmic features, respectively. We conducted a listening test based on pair-wise comparison to assess to what extent the machine-based similarity models match human perception. We found the machine-based models to differ significantly to human responses due to differences in participants' responses. The best performing model relied on features from the FANTASTIC toolkit obtaining a rank match rate with human response of 63%, while features from the SynPy toolkit only obtained a ranking match rate of 46%. While more work is needed on a stronger model of similarity, we do not believe these results prevent FANTASTIC features being used as a measure of similarity within creative systems.

Towards a Semantic Architecture for the Internet of Musical Things

“…This is achieved here by conferring Musical Things the ability to produce a simple musical accompaniment based on audio-related attributes. This could be of interest for new interfaces for musical expression and computationally creative music systems (see e.g., [69]).…”